code: def _get_logits_and_tokens(
      self, prompts: list[str]
  ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
      all_logits = []
      all_tokens = []
      for prompt in prompts:
          tokenized_inputs = self.tokenizer(
              prompt,
              return_tensors="pt",
              truncation=True,
              return_token_type_ids=False,
          ).to(self.device)
          outputs = self.model(**tokenized_inputs)
          logits = (
              outputs["logits"]
              .detach()
              .to(device="cpu", dtype=torch.float32)
          )
          # need to remove batch dimension
          all_logits.append(torch.squeeze(logits))
          all_tokens.append(
              torch.squeeze(tokenized_inputs["input_ids"])
          )
      return all_logits, all_tokens
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union, cast, Sequence
  from typing_extensions import Literal, get_args
  import numpy
  import torch
  import torch.nn.functional
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from accelerate import Accelerator
  from .isp_dataset import ClassificationExample, Example, ExampleWithClasses, LogoddsExample, SequenceProbExample, NumericExample, TaskType
  from .isp_utils import BasicParser
  from .isp_utils import OpenAIModel, OpenaAIModelList
label_desc: Use image node_id 5 for calling the HFModel obj's underlying member method code with example usage: obj._get_logits_and_tokens(prompts) and returns: all_logits,  all_tokens